Browsing CSAIL Digital Archive by Author "De Vito, Ernesto"

Now showing items 1-3 of 3

    • Adaptive Kernel Methods Using the Balancing Principle 

      Rosasco, Lorenzo; Pereverzyev, Sergei; De Vito, Ernesto (2008-10-16)
      The regularization parameter choice is a fundamental problem in supervised learning since the performance of most algorithms crucially depends on the choice of one or more of such parameters. In particular a main theoretical ...

    • Elastic-Net Regularization in Learning Theory 

      De Mol, Christine; Rosasco, Lorenzo; De Vito, Ernesto (2008-07-24)
      Within the framework of statistical learning theory we analyze in detail the so-called elastic-net regularization scheme proposed by Zou and Hastie ["Regularization and variable selection via the elastic net" J. R. Stat. ...

    • A Note on Perturbation Results for Learning Empirical Operators 

      De Vito, Ernesto; Belkin, Mikhail; Rosasco, Lorenzo (2008-08-19)
      A large number of learning algorithms, for example, spectral clustering, kernel Principal Components Analysis and many manifold methods are based on estimating eigenvalues and eigenfunctions of operators defined by a ...